
'''simple docstring'''






import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     List[Any]				):



      """simple docstring"""




      _lowerCamelCase					: List[Any]		    = torch.load(UpperCamelCase__					,						map_location="cpu"				)

      _lowerCamelCase					: Optional[int]		    = chkpt["model"]

      # We have the base model one level deeper than the original XLM repository
      _lowerCamelCase					: Optional[int]		    = {}
      for k, v in state_dict.items():
            if "pred_layer" in k:
                  _lowerCamelCase					: Optional[int]		    = v
            else:
                  _lowerCamelCase					: str		    = v

      _lowerCamelCase					: int		    = chkpt["params"]
      _lowerCamelCase					: List[str]		    = {n: v for n, v in config.items() if not isinstance(UpperCamelCase__					,						(torch.FloatTensor, numpy.ndarray)				)}

      _lowerCamelCase					: Tuple		    = chkpt["dico_word2id"]
      _lowerCamelCase					: List[Any]		    = {s + "</w>" if s.find("@@"				) == -1 and i > 13 else s.replace("@@"					,						""				): i for s, i in vocab.items()}

      # Save pytorch-model
      _lowerCamelCase					: Dict		    = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _lowerCamelCase					: Union[str, Any]		    = pytorch_dump_folder_path + "/" + CONFIG_NAME
      _lowerCamelCase					: Optional[int]		    = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

      print(F'Save PyTorch model to {pytorch_weights_dump_path}'				)
      torch.save(UpperCamelCase__					,						UpperCamelCase__				)

      print(F'Save configuration file to {pytorch_config_dump_path}'				)
      with open(UpperCamelCase__					,						"w"					,						encoding="utf-8"				) as f:
            f.write(json.dumps(UpperCamelCase__					,						indent=2				) + "\n"				)

      print(F'Save vocab file to {pytorch_config_dump_path}'				)
      with open(UpperCamelCase__					,						"w"					,						encoding="utf-8"				) as f:
            f.write(json.dumps(UpperCamelCase__					,						indent=2				) + "\n"				)


if __name__ == "__main__":
 UpperCAmelCase_							: Tuple =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 UpperCAmelCase_							: List[str] =							parser.parse_args()
 convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    __lowerCamelCase            =    0
    while len(UpperCamelCase__							) > 1:
        __lowerCamelCase            =    0
        # Consider two files with minimum cost to be merged
        for _ in range(2							):
            __lowerCamelCase            =    files.index(min(UpperCamelCase__							)							)
            temp += files[min_index]
            files.pop(UpperCamelCase__							)
        files.append(UpperCamelCase__							)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
  import doctest

  doctest.testmod()
